Vighnesh Birodkar
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- Multimodal Machine Learning Applications 2
- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 1
- Generative Adversarial Networks and Image Synthesis 1
- Human Pose and Action Recognition 1
- Digital Media Forensic Detection 1
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- Adversarial Robustness in Machine Learning 2
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Emily DentonVincent DumoulinCristina Nader VasconcelosZhichao LuSiyang LiJonathan HuangVivek RathodNatasha Jaques
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceComputer Graphics and Computer-Aided Design
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2 papers)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesGermanyIsrael
In The Last Decade
Vighnesh Birodkar
4 papers receiving 162 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 141
- Artificial Intelligence 60
- Computer Graphics and Computer-Aided Design 6
- Signal Processing 14
- Media Technology 8
Countries citing papers authored by Vighnesh Birodkar
This map shows the geographic impact of Vighnesh Birodkar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vighnesh Birodkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vighnesh Birodkar more than expected).
Fields of papers citing papers by Vighnesh Birodkar
This network shows the impact of papers produced by Vighnesh Birodkar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vighnesh Birodkar. The network helps show where Vighnesh Birodkar may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Vighnesh Birodkar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 24 | |
| 2 | 2022 | 12 | |
| 3 | 2021 | 17 | |
| 4 | Unsupervised Learning of Disentangled Representations from Video | 2017 | 115 |
About Vighnesh Birodkar
Vighnesh Birodkar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases, having authored 4 papers that have together received 168 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Human Pose and Action Recognition (1 paper) and Digital Media Forensic Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (141 citations), Artificial Intelligence (60 citations) and Computer Graphics and Computer-Aided Design (6 citations). Vighnesh Birodkar has collaborated with scholars based in United States, Germany and Israel. Frequent co-authors include Emily Denton, Vincent Dumoulin, Cristina Nader Vasconcelos, Zhichao Lu, Siyang Li, Jonathan Huang, Vivek Rathod, Natasha Jaques, Austin Waters and Peter J. Anderson. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Neural Information Processing Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.